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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Prediction of the critical reduced electric field strength for carbon dioxide and its mixtures with copper vapor from Boltzmann analysis for a gas temperature range of 300 K to 4000 K at 0.4 MPa

The influence of copper vapor mixed in hot CO{sub 2} on dielectric breakdown properties of gas mixture at a fixed pressure of 0.4 MPa for a temperature range of 300 K–4000 K is numerically analyzed. First, the equilibrium composition of hot CO{sub 2} with different copper fractions is calculated using a method based on mass action law. The next stage is devoted to computing the electron energy distribution functions (EEDF) by solving the two-term Boltzmann equation. The reduced ionization coefficient, the reduced attachment coefficient, and the reduced effective ionization coefficient are then obtained based on the EEDF. Finally, the critical reduced electric field (E/N){sub cr} is obtained. The results indicate that an increasing mole fraction of copper markedly reduces (E/N){sub cr} of the CO{sub 2}–Cu gas mixtures because of copper's low ionization potential and large ionization cross section. Additionally, the generation of O{sub 2} from the thermal dissociation of CO{sub 2} contributes to the increase of (E/N){sub cr} of CO{sub 2}–Cu hot gas mixtures from about 2000 K to 3500 K.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Stochastic evaluation of four-component relativistic second-order many-body perturbation energies: A potentially quadratic-scaling correlation method

A second-order many-body perturbation correction to the relativistic Dirac-Hartree-Fock energy is evaluated stochastically by integrating 13-dimensional products of four-component spinors and Coulomb potentials. The integration in the real space of electron coordinates is carried out by the Monte Carlo (MC) method with the Metropolis sampling, whereas the MC integration in the imaginary-time domain is performed by the inverse-CDF (cumulative distribution function) method. The computational cost to reach a given relative statistical error for spatially compact but heavy molecules is observed to be no worse than cubic and possibly quadratic with the number of electrons or basis functions. This is a vast improvement over the quintic scaling of the conventional, deterministic second-order many-body perturbation method. The algorithm is also easily and efficiently parallelized with demonstrated 92% strong scalability going from 64 to 4096 processors for a fixed job size.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Equity and modeling in sustainability science: Examples and opportunities throughout the process

Equity is core to sustainability, but current interventions to enhance sustainability often fall short in adequately addressing this linkage. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. This Perspective highlights progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions. We present a conceptual framework for equity in systems modeling, focused on its distributional, procedural, and recognitional dimensions. We discuss examples of how modelers engage with these different dimensions throughout the modeling process and from across a range of modeling approaches and topics, including water resources, energy systems, air quality, and conservation. Synthesizing across these examples, we identify significant advances in enhancing procedural and recognitional equity by reframing models as tools to explore pluralism in worldviews and knowledge systems; enabling models to better represent distributional inequity through new computational techniques and data sources; investigating the dynamics that can drive inequities by linking different modeling approaches; and developing more nuanced metrics for assessing equity outcomes. We also identify important future directions, such as an increased focus on using models to identify pathways to transform underlying conditions that lead to inequities and move toward desired futures. By looking at examples across the diverse fields within sustainability science, we argue that there are valuable opportunities for mutual learning on how to use models more effectively as tools to support sustainable and equitable futures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

ML-Based Pebble Power Reconstruction for Pebble Bed Reactor Analysis

Pebble power reconstruction has been explored to complement the conventional homogenized modeling approach in pebble bed reactor (PBR) analysis, as detailed heterogeneous geometry calculations are computationally expensive. The random distribution of pebble fuels within the core challenges the application of conventional pin power reconstruction methods. To address this, we introduce a machine learning approach based on the transformer model, composed of encoder and decoder layers, to estimate the flux and power form functions for reconstructing individual pebble neutron fluxes and powers. The homogeneous neutron flux distribution within each spectral zone (SZ) is obtained from finite element solutions of global diffusion or transport calculations. Verification tests demonstrate that the trained transformer model accurately predicts power form functions over a range of conditions, including variations in pebble enrichment, location, type, SZ size, and burnup. In particular, verification using a three-dimensional PBR benchmark with burned pebbles shows good agreement in heterogeneous pebble power distributions between Griffin and Serpent. These results highlight the potential of applying conventional pin power reconstruction approaches to PBR cores with randomly distributed pebbles.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Analysis of Repeatability and Reproducibility Studies With Ordinal Measurements

A Bayesian inferential approach with a noninformative prior is introduced to analyze ordinal repeatability and reproducibility (R&R) data using the De Mast–Van Wieringen model. This approach is extended with a weakly informative prior and random effects to allow for the consideration of a population of raters and prediction of a new rater. This random-effects approach is also shown to result in partial pooling of estimates across raters. In addition, match-probability-based measures to decompose ordinal R&R study data into contributions due to repeatability and due to reproducibility are defined. All extensions involving Bayesian inference (for fixed or random effects) and measures are illustrated on real and simulated ordinal R&R study data and are applicable in business and industry settings. This methodology can be implemented using the supplemental R package ordinalRR available from CRAN. Additional supplementary material for this article is available online.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Bremsstrahlung gamma rays from light dark matter

We discuss the often-neglected role of bremsstrahlung processes on the interstellar gas in computing indirect signatures of Dark Matter (DM) annihilation in the Galaxy, particularly for light DM candidates in the phenomenologically interesting O(10) GeV mass range. Especially from directions close to the Galactic Plane, the γ-ray spectrum is altered via two effects: directly, by the photons emitted in the bremsstrahlung process by energetic electrons which are among the DM annihilation byproducts; indirectly, by the modification of the same electron spectrum, due to the additional energy loss process in the diffusion-loss equation (e.g. the resulting inverse Compton emission is altered). We quantify the importance of the bremsstrahlung emission in the GeV energy range, showing that it is sometimes the dominant component of the γ-ray spectrum. We also find that, in regions in which bremsstrahlung dominates energy losses, the related γ-ray emission is only moderately sensitive to possible large variations in the gas density. Still, we stress that, for computing precise spectra in the (sub-)GeV range, it is important to obtain a reliable description of the Galaxy gas distribution as well as to compute self-consistently the γ-ray emission and the solution to the diffusion-loss equation. For example, these are crucial issues to quantify and interpret meaningfully γ-ray map 'residuals' in the inner Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Tracing the cosmic velocity field at z∼ 0.1 from galaxy luminosities in the SDSS DR7

Spatial modulations in the distribution of observed luminosities (computed using redshifts) of ∼ 5× 10{sup 5} galaxies from the SDSS Data Release 7, probe the cosmic peculiar velocity field out to z∼ 0.1. Allowing for luminosity evolution, the r-band luminosity function, determined via a spline-based estimator, is well represented by a Schechter form with M{sup *}(z)-5 log{sub 10} h = -20.52 -1.6(z-0.1)± 0.05 and α{sup *} = -1.1± 0.03. Bulk flows and higher velocity moments in two redshift bins, 0.02 < z < 0.07 and 0.07 < z < 0.22, agree with the predictions of the ΛCDM model, as obtained from mock galaxy catalogs designed to match the observations. Assuming a ΛCDM model, we estimate σ{sub 8} ≈ 1.1± 0.4 for the amplitude of the linear matter power spectrum, where the low accuracy is due to the limited number of galaxies. While the low z bin is robust against coherent photometric uncertainties, the bias of results from the second bin is consistent with the ∼1% magnitude tilt reported by the SDSS collaboration. The systematics are expected to have a significantly lower impact in future datasets with larger sky coverage and better photometric calibration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Blinking statistics and molecular counting in direct stochastic reconstruction microscopy (dSTORM)

Abstract Motivation Many recent advancements in single-molecule localization microscopy exploit the stochastic photoswitching of fluorophores to reveal complex cellular structures beyond the classical diffraction limit. However, this same stochasticity makes counting the number of molecules to high precision extremely challenging, preventing key insight into the cellular structures and processes under observation. Results Modelling the photoswitching behaviour of a fluorophore as an unobserved continuous time Markov process transitioning between a single fluorescent and multiple dark states, and fully mitigating for missed blinks and false positives, we present a method for computing the exact probability distribution for the number of observed localizations from a single photoswitching fluorophore. This is then extended to provide the probability distribution for the number of localizations in a direct stochastic optical reconstruction microscopy experiment involving an arbitrary number of molecules. We demonstrate that when training data are available to estimate photoswitching rates, the unknown number of molecules can be accurately recovered from the posterior mode of the number of molecules given the number of localizations. Finally, we demonstrate the method on experimental data by quantifying the number of adapter protein linker for activation of T cells on the cell surface of the T-cell immunological synapse. Availability and implementation Software and data available at https://github.com/lp1611/mol_count_dstorm. Supplementary information Supplementary data are available at Bioinformatics online.

Patel, Lekha↗
DOE OSTI2025

Fast calculation of diffraction patterns from an ensemble of aligned molecules

We report an algorithm to calculate electron diffraction patterns for molecules with anisotropic angular distribution, which is significantly faster than existing methods. The algorithm uses a transform to convert the molecular orientation distribution, which is a function of three Euler angles, to the atom-pair distribution functions which depend on the polar and azimuthal angles. The diffraction signal can then be calculated from the atom-pair distributions. We demonstrate the computation method numerically by calculating electron diffraction patterns for a symmetric top molecule (trifluoroiodomethane) and an asymmetric top molecule (formaldehyde) and show that it reduces the calculation time by approximately two orders of magnitude compared to the standard brute-force method. Here, the method can also be applied to the calculation of x-ray diffraction patterns.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Model for scattering with proliferating resonances: Many coupled square wells

We present a multichannel model for elastic interactions, composed of an arbitrary number of coupled finite square-well potentials, and derive semianalytic solutions for its scattering behavior. Despite the model's simplicity, it is flexible enough to include many coupled short-ranged resonances in the vicinity of the collision threshold, as is necessary to describe ongoing experiments in ultracold molecules and lanthanide atoms. Here, we also introduce a simple but physically realistic statistical ensemble for parameters in this model. We compute the resulting probability distributions of nearest-neighbor resonance spacings and analyze them by fitting to the Brody distribution. We quantify the ability of alternative distribution functions, for resonance spacing and resonance number variance, to describe the crossover regime. The analysis demonstrates that the multichannel square-well model with the chosen ensemble of parameters naturally captures the crossover from integrable to chaotic scattering as a function of closed-channel coupling strength.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

From Reproducible Edge–Cloud Experimentation to Real-World Practice: The E2Clab Experience

Reproducibility is already difficult in distributed systems; on the computing continuum, it becomes substantially harder. Applications that span sensing devices, edge and fog resources, and cloud platforms must be evaluated across heterogeneous hardware, variable network conditions, cross-layer orchestration decisions, and long-running workflow lifecycles. We use E2Clab as a case study to examine these challenges and their implications for experimental methodology. We explain why reproducible experimentation is harder on the continuum, then revisit E2Clab as an initial response based on explicit modeling of infrastructure, workflow lifecycle, and artifacts. Lastly, we discuss how its evolution toward more realistic application settings can be understood through the lens of Translational Computer Science. We argue that reproducible continuum experimentation requires methods that are rigorous enough for research while remaining adaptable to real-world practice.

42 ENGINEERING↗
DOE OSTI2011

Determination of effective axion masses in the helium-3 buffer of CAST

The CERN Axion Solar Telescope (CAST) is a ground based experiment located in Geneva (Switzerland) searching for axions coming from the Sun. Axions, hypothetical particles that not only could solve the strong CP problem but also be one of the favored candidates for dark matter, can be produced in the core of the Sun via the Primakoff effect. They can be reconverted into X-ray photons on Earth in the presence of strong electromagnetic fields. In order to look for axions, CAST points a decommissioned LHC prototype dipole magnet with different X-ray detectors installed in both ends of the magnet towards the Sun. The analysis of the data acquired during the first phase of the experiment yielded the most restrictive experimental upper limit on the axion-to-photon coupling constant for axion masses up to about 0.02 eV/c{sup 2}. During the second phase, CAST extends its mass sensitivity by tuning the electron density present in the magnetic field region. Injecting precise amounts of helium gas has enabled CAST to look for axion masses up to 1.2 eV/c{sup 2}. This paper studies the determination of the effective axion masses scanned at CAST during its second phase. The use of a helium gas buffer at temperatures of 1.8 K has required a detailed knowledge of the gas density distribution. Complete sets of computational fluid dynamic simulations validated with experimental data have been crucial to obtain accurate results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Deep Reinforcement Learning for Distribution System Restoration Using Distributed Energy Resources and Tie-Switches

Distributed energy resources (DERs), such as solar PVs and energy storage, can be used to restore distribution system critical loads after the extreme weather events to increase grid resilience. However, coordinating multiple DERs together with tie-switches for multi-step restoration process under renewable uncertainty is challenging. This paper proposes a deep reinforcement learning to control discrete actions of switching on/off tie switches and DERs for critical load restoration. The restoration problem is first cast into the Markov decision process suitable for DRL. Then, the original soft actor critic (SAC) method for continuous actions has been extended to handle discrete and continuous actions. Numerical comparison results with other stochastic optimization-based approaches on the modified IEEE 33-bus system show that the proposed method can achieve fast critical load restoration in the presence of substation power outage while maintaining system voltage limit throughout the restoration process.

active distribution systems↗